Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation (Paperback)

Atwan, Tarek A.

  • 出版商: Packt Publishing
  • 出版日期: 2022-06-30
  • 售價: $2,030
  • 貴賓價: 9.5$1,929
  • 語言: 英文
  • 頁數: 630
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1801075549
  • ISBN-13: 9781801075541
  • 相關分類: Python程式語言Data Science
  • 海外代購書籍(需單獨結帳)

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商品描述

Perform time series analysis and forecasting confidently with this Python code bank and reference manual

 

Key Features:

  • Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
  • Learn different techniques for evaluating, diagnosing, and optimizing your models
  • Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities

 

Book Description:

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.

This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch.

Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.

 

What You Will Learn:

  • Understand what makes time series data different from other data
  • Apply various imputation and interpolation strategies for missing data
  • Implement different models for univariate and multivariate time series
  • Use different deep learning libraries such as TensorFlow, Keras, and PyTorch
  • Plot interactive time series visualizations using hvPlot
  • Explore state-space models and the unobserved components model (UCM)
  • Detect anomalies using statistical and machine learning methods
  • Forecast complex time series with multiple seasonal patterns

 

Who this book is for:

This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

商品描述(中文翻譯)

以Python程式碼庫和參考手冊自信地進行時間序列分析和預測。

主要特點:
- 使用統計、機器學習和深度學習算法探索預測和異常檢測技術。
- 學習評估、診斷和優化模型的不同技術。
- 處理具有趨勢、多個季節模式和不規則性的各種複雜數據。

書籍描述:
時間序列數據無處不在,頻率和數量都很高。它復雜且可能包含噪音、不規則性和多個模式,因此熟悉本書中涵蓋的數據準備、分析和預測技術至關重要。

本書介紹了處理時間序列數據的實用技術,從各種來源和格式中提取時間序列數據開始,無論是在私有雲存儲、關聯數據庫、非關聯數據庫還是專門的時間序列數據庫(如InfluxDB)中。接下來,您將學習處理缺失數據的策略,處理時區和自定義工作日,並使用直觀的統計方法檢測異常,然後使用更高級的無監督機器學習模型。本書還將探討使用傳統統計模型(如Holt-Winters、SARIMA和VAR)進行預測。這些技巧將介紹處理非穩態數據的實用技術,使用功率轉換、ACF和PACF圖以及分解具有多個季節模式的時間序列數據。之後,您將使用TensorFlow和PyTorch等工具進行機器學習和深度學習模型的工作。

最後,您將學習如何使用本書中介紹的技巧評估、比較和優化模型等。

學到什麼:
- 了解時間序列數據與其他數據的不同之處。
- 應用各種插補和內插策略處理缺失數據。
- 實現單變量和多變量時間序列的不同模型。
- 使用TensorFlow、Keras和PyTorch等不同的深度學習庫。
- 使用hvPlot繪製交互式時間序列可視化。
- 探索狀態空間模型和未觀察到的組件模型(UCM)。
- 使用統計和機器學習方法檢測異常。
- 預測具有多個季節模式的複雜時間序列。

本書適合數據分析師、業務分析師、數據科學家、數據工程師或Python開發人員,他們希望獲得時間序列分析和預測技術的實用Python配方。需要基本的Python編程知識。雖然具備基本的數學和統計背景會有益,但不是必需的。先前使用時間序列數據解決業務問題的經驗也有助於更好地利用和應用本書中的不同配方。